import pandas as pd
from collections import deque
from transformers import AutoModelForCausalLM, pipeline
import re

import os

os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"


# 模拟的WebSocket客户端类，实际使用时需要替换为真实的实现
class WebSocketClient:
    def __init__(self):
        pass

    def connect(self):
        pass

    def receive_data(self):
        # 模拟接收数据，实际中应从真实的WebSocket连接接收数据
        return {'price': 100, 'volume': 1000, 'open_interest': 100, 'news_text': 'Market is stable',
                'technical_indicators': 'MA5: 101, MA10: 102'}


# 大模型策略生成模块
class LLMTradingStrategy:
    def __init__(self, model_name="gpt-4"):
        # 这里使用模拟，实际需要使用相应的大模型库加载模型
        self.model = None
        self.prompt_template = """
        分析以下市场数据，生成日内交易策略：
        1. 最新价: {price}
        2. 成交量: {volume}
        3. 持仓量变化: {open_interest}
        4. 新闻情绪: {news_sentiment}
        5. 技术指标: {technical_indicators}
        """

    def generate_strategy(self, market_data):
        # 进行新闻情绪分析
        news_analyzer = NewsAnalyzer()
        market_data['news_sentiment'] = news_analyzer.analyze(market_data['news_text'])
        # 生成提示词
        prompt = self.prompt_template.format(**market_data)
        # 这里模拟模型生成策略，实际需要调用模型的生成方法
        response = "Buy if price > 105, Sell if price < 95"
        return self.parse_strategy(response)

    def parse_strategy(self, response):
        # 简单解析策略，实际情况可能需要更复杂的解析逻辑
        buy_condition = re.search(r'Buy if (.*)', response)
        sell_condition = re.search(r'Sell if (.*)', response)
        return {
            'buy_condition': buy_condition.group(1) if buy_condition else None,
            'sell_condition': sell_condition.group(1) if sell_condition else None
        }


# 实时数据处理管道
class RealTimeDataProcessor:
    def __init__(self):
        self.websocket = WebSocketClient()
        self.ohlcv_queue = deque(maxlen=100)

    def process(self):
        # 从WebSocket接收原始数据
        raw_data = self.websocket.receive_data()
        # 清理数据，这里简单返回原始数据，实际中可添加更多清理逻辑
        cleaned_data = self.clean(raw_data)
        # 提取特征，这里简单返回清理后的数据，实际可添加更多特征提取逻辑
        features = self.extract_features(cleaned_data)
        self.ohlcv_queue.append(features)
        # 计算技术指标，这里简单返回特征数据，实际可添加指标计算逻辑
        return self.calculate_indicators(self.ohlcv_queue)

    def clean(self, raw_data):
        return raw_data

    def extract_features(self, cleaned_data):
        return cleaned_data

    def calculate_indicators(self, ohlcv_queue):
        return ohlcv_queue[-1]


# 新闻情绪分析类
class NewsAnalyzer:
    def __init__(self):
        # 使用预训练的金融情感分析模型
        self.sentiment_analyzer = pipeline("text-classification", model="ProsusAI/finbert")

    def analyze(self, news_text):
        # 进行情感分析
        results = self.sentiment_analyzer(news_text)
        # 返回得分最高的情感标签
        return max(results, key=lambda x: x['score'])['label']


# 风险管理系统
class RiskManager:
    def __init__(self):
        self.position_limit = 1000000  # 持仓限额
        self.stop_loss_pct = 0.02  # 止损比例
        self.max_drawdown = 0.1  # 最大回撤容忍度

    def check_risk(self, portfolio, order):
        # 检查是否超过持仓限额
        if portfolio['value'] * order['size'] > self.position_limit:
            raise ValueError("Exceed position limit")
        # 检查止损是否合理
        if order['stop_loss'] < portfolio['value'] * (1 - self.stop_loss_pct):
            raise ValueError("Invalid stop loss")
        return True


# 交易引擎类
class TradingEngine:
    def __init__(self):
        self.strategy = LLMTradingStrategy()
        self.data_processor = RealTimeDataProcessor()
        self.risk_manager = RiskManager()
        self.portfolio = {'value': 1000000, 'position': 0}

    def run(self):
        while True:
            # 处理实时数据
            market_data = self.data_processor.process()
            # 生成交易策略
            strategy = self.strategy.generate_strategy(market_data)
            # 根据策略生成订单
            order = self.generate_order(strategy, market_data)
            if order:
                try:
                    # 检查订单的风险
                    self.risk_manager.check_risk(self.portfolio, order)
                    # 执行订单，这里简单打印，实际中需要连接交易接口执行
                    self.execute_order(order)
                except ValueError as e:
                    print(f"Risk violation: {e}")

    def generate_order(self, strategy, market_data):
        price = market_data['price']
        order = None
        if strategy['buy_condition'] and eval(strategy['buy_condition'], {'price': price}):
            order = {'type': 'buy', 'size': 100, 'stop_loss': price * (1 - self.risk_manager.stop_loss_pct)}
        elif strategy['sell_condition'] and eval(strategy['sell_condition'], {'price': price}):
            order = {'type': 'sell', 'size': 100, 'stop_loss': None}
        return order

    def execute_order(self, order):
        if order['type'] == 'buy':
            self.portfolio['position'] += order['size']
            self.portfolio['value'] -= order['size'] * order['stop_loss']
            print(f"Buy {order['size']} shares at stop loss {order['stop_loss']}")
        elif order['type'] == 'sell':
            self.portfolio['position'] -= order['size']
            print(f"Sell {order['size']} shares")


if __name__ == "__main__":
    engine = TradingEngine()
    engine.run()
